@@ -77,10 +77,10 @@ prices are available for up to 5 years:
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import pandas_datareader.data as web
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from datetime import datetime
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- start = datetime(2015 , 2 , 9 )
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- end = datetime(2017 , 5 , 24 )
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+ start = datetime(2016 , 9 , 1 )
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+ end = datetime(2018 , 9 , 1 )
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f = web.DataReader(' F' , ' iex' , start, end)
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- f.loc[' 2015-02-09 ' ]
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+ f.loc[' 2018-08-31 ' ]
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There are additional interfaces to this API that are
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directly exposed: tops (`'iex-tops' `) and last (`'iex-lasts' `).
@@ -109,8 +109,7 @@ year relative to today.
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f = web.DataReader(' F' , ' robinhood' )
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f.head()
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-
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- .. _remote_data.alphavantage
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+ .. _remote_data.alphavantage :
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Alpha Vantage
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=============
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start = datetime.datetime(2010 , 1 , 1 )
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end = datetime.datetime(2013 , 1 , 27 )
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gdp = web.DataReader(' GDP' , ' fred' , start, end)
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- gdp.ix [' 2013-01-01' ]
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+ gdp.loc [' 2013-01-01' ]
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# Multiple series:
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inflation = web.DataReader([' CPIAUCSL' , ' CPILFESL' ], ' fred' , start, end)
@@ -305,7 +304,7 @@ World Bank
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by using the ``wb `` I/O functions.
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Indicators
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- ----------
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+ ^^^^^^^^^^
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Either from exploring the World Bank site, or using the search function included,
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every world bank indicator is accessible.
@@ -316,7 +315,7 @@ constant dollars in North America, you would use the ``search`` function:
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.. code-block :: python
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In [1 ]: from pandas_datareader import wb
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- In [2 ]: mathces = wb.search(' gdp.*capita.*const' )
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+ In [2 ]: matches = wb.search(' gdp.*capita.*const' )
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Then you would use the ``download `` function to acquire the data from the World
@@ -417,7 +416,7 @@ populations in rich countries tend to use cellphones at a higher rate:
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==============================================================================
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Country Codes
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- ------------ -
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+ ^^^^^^^^^^^^^
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The `` country`` argument accepts a string or list of mixed
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`two < http:// en.wikipedia.org/ wiki/ ISO_3166 - 1_alpha - 2 > ` __ or `three < http:// en.wikipedia.org/ wiki/ ISO_3166 - 1_alpha - 3 > ` __ character
@@ -426,7 +425,7 @@ ISO country codes, as well as dynamic `World Bank exceptions <https://datahelpde
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For a list of the the hard- coded country codes (used solely for error handling logic) see `` pandas_datareader.wb.country_codes`` .
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Problematic Country Codes & Indicators
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- --------------------------------------
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+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. note::
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@@ -533,7 +532,7 @@ available. More information on the `field <http://www.nasdaqtrader.com/trader.as
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In [12 ]: from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
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In [13 ]: symbols = get_nasdaq_symbols()
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- In [14 ]: print (symbols.ix [' IBM' ])
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+ In [14 ]: print (symbols.loc [' IBM' ])
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Nasdaq Traded True
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Security Name International Business Machines Corporation Co...
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Listing Exchange N
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